Welcome
Developer Guide
Data Handler Overview
Data Handler has four different APIs, and three of the APIs represent a different method uploading data.

Release Notes
Installation Guide
Developer Guide
- Overview
  To access the Marketing AION REST API interface, use the Docker Desktop application to start the Docker container.
- Tracker
- API Status
  Use these APIs to get the status and set the status of the different types of API.
- Data Handler Overview
  Data Handler has four different APIs, and three of the APIs represent a different method uploading data.
  - Uploading Data by Browser
    Use this API endpoint to upload data and initiate usecase generation. The data is acquired through browsing file from the system for training. It provides functionalities to handle data uploads, generate usecases, and update API statuses. It allows creation of a derived column based on two existing columns and an operation (Add, Subtract, Multiply, Divide). It also handles data type validation to ensure operations are performed on integer columns. For additional information, see the attached README file.
  - Uploading Data from NiFi
    Use this API endpoint allows to upload data and initiate usecase generation. The data is acquired from NiFi (an integrated data logistics platform that automates the flow of data between systems.) for training. It provides functionalities to handle data uploads, generate usecases, and update API statuses.
  - Uploading Data from URL
    This API endpoint allows you to upload data and initiate usecase generation. The data for training is acquired from an URL hosting the dataset. It provides functionalities to handle data uploads, generate usecases, and update API statuses.
  - Issue Spotter
    Use this API to detect issues in datasets like missing data, duplicate row, duplicate column, constant value, etc. To detect issues, you must provide the file path.
- Training
  For Training, you can execute either of the generic training by using Train for pipeline API.
- Prediction
  The Prediction section lists APIs that are used for handling single prediction and batch prediction.
- Model Inference (beta)
- Download-model
  These APIs are used to download Python package, Docker container package, and Mlac.
- Monitoring
  Monitoring ML models checks for changes in data distribution ensuring that data is maintaining an acceptable level of variation.
- Xplain
  Explainability gives an explanation about Prediction and Model.
- Usecases
  Use the following APIs to retrieve the Use Case list, to delete use cases, and to retrieve the logs.
- EDA
- AutoPilot
  This Autopilot Mode refers to a feature that allows users to build an automated model without direct human intervention.
- Data Generator
- Building Usecases with API
  To build a usecase or to train a model, complete the following steps:
MLAC User Guide
User Guide

Data Handler Overview

Data Handler has four different APIs, and three of the APIs represent a different method uploading data.

The different methods of uploading data are:

Using a local machine
From NiFi
From URL

The Issue Spotter API is used to find abnormalities, if any, in the uploaded dataset.

To add feedback about this topic, select the following checkbox:

By clicking this box, you acknowledge that you are NOT a U.S. Federal Government employee or agency, nor are you submitting information with respect to or on behalf of one. HCL provides software and services to U.S. Federal Government customers through its partners Four, Inc. Contact this team at https://hcltechsw.com/resources/us-government-contact. Do not include any personal data in this Comment box. Note: To report a problem or question about the product software, do not use this form. Instead, go to the HCL Software Support site. Personal data should not be shared in this comment box. See our Privacy Statement to understand how personal data is used.